Delivery time prediction on autopilot:
a real estimate, not a courier's default
Most stores show every customer the same generic delivery window regardless of warehouse, carrier or destination, which is why the estimate is so often wrong. We build a model that predicts delivery time from your actual shipping history, route by route, carrier by carrier, and flags an order early if it is tracking toward a late delivery.
The process today
A delivery estimate shown at checkout is usually a flat default tied to a shipping tier, same-day, standard, express, applied uniformly regardless of which warehouse the order ships from, which carrier handles that specific route, or what that carrier’s actual track record looks like on it. The estimate is a promise built on policy, not on what has actually happened on previous shipments down that exact path.
The mismatch surfaces as a steady trickle of customer complaints and support tickets about late deliveries, each one investigated individually, when the underlying pattern, this carrier is consistently two days slower on this particular route, this warehouse’s weekend dispatch adds a day nobody accounts for, is visible in the data and has been for a while. Nobody has connected it because nobody is systematically comparing promised versus actual delivery time at the route level.
The second cost is that a late delivery is usually discovered by the customer first, who reaches out asking where their order is, rather than by the store, which could have seen it coming from the tracking data days earlier and proactively reached out instead of reacting to a complaint.
What the agent does
The model trains on your own shipment history, carrier, route, warehouse, and predicts a delivery window specific to that combination rather than a flat policy default. The checkout page and order status page can show a tighter, more honest estimate for routes with a strong track record and a wider one for routes that are genuinely less predictable, instead of the same number for everyone.
Once an order ships, the model compares its tracking progress against the typical pattern for that route and flags it early if it is tracking toward a late delivery, while there is still time to proactively notify the customer, offer a partial refund on shipping, or in some cases escalate with the carrier before the promised window actually passes. Carrier performance is tracked by route over time, so an operations team can see which carrier is genuinely faster and more reliable on which lanes, often a useful input for carrier selection on its own.
When an ETA shifts meaningfully for an order already in transit, a customer-facing update can go out automatically, so the customer hears about a delay from the store before they have to ask.
What stays with humans
Choosing which carrier to use for a given route, negotiating carrier contracts, and deciding how to compensate a customer for a genuinely late delivery stay with your operations team. The model predicts and flags; it does not change a carrier assignment or issue a refund on its own.
Guards
Every prediction is logged against the actual delivery outcome, so accuracy by route and carrier is tracked over time rather than assumed. A backtest against your last three to six months of deliveries runs before go-live, and a kill switch reverts checkout and status pages to your previous flat estimates in one message.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $800 | Main carriers and routes, model, early-flag alerts | 7 to 12 days |
| Department package | from $2,500 | Delivery prediction plus automated customer notifications | 2 to 4 weeks |
Running cost is usually $20 to $70 a month depending on shipment volume.
Related
Pair this with shipment tracking updates so the customer-facing notification layer is already in place, and with order status answers so a customer asking “where is my order” gets the model’s current ETA directly. The full package breakdown is on the AI agents service page and the automation-everything overview; for a real instant-delivery operation, see the Telegram marketplace instant delivery case study and the Thailand D2C rebuild case study.
Ready to show customers an ETA based on what actually happens, not a policy default? Get in touch and we will look at your shipping history in the first call.
Tired of doing this by hand? We can take the whole routine off your team, not just this step: Routine takeover, from $400 →
FAQ
How much does delivery time prediction cost?
From $800 for a model covering your main carriers and routes, live in 7 to 12 days. A department package adding automated customer notifications usually starts at $2,500.
How is this different from the ETA our carrier already gives us?
A carrier's own estimate is often a flat default for a service tier, not adjusted for your specific warehouse, packaging time or the carrier's actual historical performance on that exact route. This model learns from what actually happened on your past shipments.
What happens when an order is tracking late?
It gets flagged early, while there is still time to proactively notify the customer or expedite, instead of the first signal being a complaint after the promised window has already passed.
Can it compare carriers for us?
Yes. The model tracks each carrier's actual performance by route, which is often useful on its own for deciding which carrier to default to for a given destination, independent of the prediction feature.
What data does it need?
Your historical shipment data: carrier, route, ship date and actual delivery date. A few months of history is enough for a first version, more sharpens it further.